Machine learning builds a model based on training data. This does not require explicit knowledge represention of rules or facts, instead the algorithm creates its own representation. Some machine learning algorithms produce human-undertandable representations, such as decison trees, others, including neural neyworks are harder to interpret and may need explainable AI technques.
Machine learning typically progresses in three phases: training phase, validation phase and application phase.
Used in Chap. 1: page 7; Chap. 2: page 23; Chap. 3: page 25; Chap. 6: pages 81, 84, 85; Chap. 7: pages 92, 93, 95, 97, 98, 99; Chap. 8: pages 104, 107, 108, 109; Chap. 10: pages 132, 133, 134, 136, 137, 139, 140, 143; Chap. 11: pages 147, 148; Chap. 12: page 183; Chap. 13: page 202; Chap. 14: pages 206, 217, 218; Chap. 16: pages 236, 239, 247, 248; Chap. 17: page 250; Chap. 18: pages 271, 277, 280, 282, 283, 291; Chap. 19: pages 293, 294, 295, 298, 299, 301, 302, 304, 306; Chap. 20: pages 313, 314, 315, 316, 317, 322, 323, 324, 326, 327; Chap. 21: pages 331, 332, 334; Chap. 22: pages 346, 351, 352, 353, 354; Chap. 23: pages 365, 368, 369; Chap. 24: page 376
Phases of machine learning